Description
An in-depth study of machine learning, to impart an understanding of the major topics in this area, the capabilities and limitations of existing methods, and research topics in this field.
Ahmed Elgammal, Haym Hirsh, Michael Littman
Credits: 3
Category: B
Prerequisites: 16:198:530
Semesters Offered:Spring
Topics: Inductive learning, including decision-tree and neural-network approaches, Bayesian methods, computational learning theory, instance-based learning, explanation-based learning, reinforcement learning, nearest neighbor methods, PAC-learning, inductive logic programming, genetic algorithms, unsupervised learning, linear and nonlinear dimensionality reduction, and kernels methods.
Expected Work: Regular readings; occasional assignments; in-class presentations; midterm and final examination and/or a course project.
